# coding=utf-8

# Copyright 2023 The HuggingFace Inc. team.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

from typing import Dict, Union

import torch

import torch.nn.functional as F

from torch import nn

from torch.utils.checkpoint import checkpoint

from ...configuration_utils import ConfigMixin, register_to_config

from ...loaders import PeftAdapterMixin

from ..attention import BasicTransformerBlock, SkipFFTransformerBlock

from ..attention_processor import (

ADDED_KV_ATTENTION_PROCESSORS,

CROSS_ATTENTION_PROCESSORS,

AttentionProcessor,

AttnAddedKVProcessor,

AttnProcessor,

)

from ..embeddings import TimestepEmbedding, get_timestep_embedding

from ..modeling_utils import ModelMixin

from ..normalization import GlobalResponseNorm, RMSNorm

from ..resnet import Downsample2D, Upsample2D

class UVit2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):

_supports_gradient_checkpointing = True

@register_to_config

def __init__(

self,

# global config

hidden_size: int = 1024,

use_bias: bool = False,

hidden_dropout: float = 0.0,

# conditioning dimensions

cond_embed_dim: int = 768,

micro_cond_encode_dim: int = 256,

micro_cond_embed_dim: int = 1280,

encoder_hidden_size: int = 768,

# num tokens

vocab_size: int = 8256, # codebook_size + 1 (for the mask token) rounded

codebook_size: int = 8192,

# `UVit2DConvEmbed`

in_channels: int = 768,

block_out_channels: int = 768,

num_res_blocks: int = 3,

downsample: bool = False,

upsample: bool = False,

block_num_heads: int = 12,

# `TransformerLayer`

num_hidden_layers: int = 22,

num_attention_heads: int = 16,

# `Attention`

attention_dropout: float = 0.0,

# `FeedForward`

intermediate_size: int = 2816,

# `Norm`

layer_norm_eps: float = 1e-6,

ln_elementwise_affine: bool = True,

sample_size: int = 64,

):

super().__init__()

self.encoder_proj = nn.Linear(encoder_hidden_size, hidden_size, bias=use_bias)

self.encoder_proj_layer_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)

self.embed = UVit2DConvEmbed(

in_channels, block_out_channels, vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias

)

self.cond_embed = TimestepEmbedding(

micro_cond_embed_dim + cond_embed_dim, hidden_size, sample_proj_bias=use_bias

)

self.down_block = UVitBlock(

block_out_channels,

num_res_blocks,

hidden_size,

hidden_dropout,

ln_elementwise_affine,

layer_norm_eps,

use_bias,

block_num_heads,

attention_dropout,

downsample,

False,

)

self.project_to_hidden_norm = RMSNorm(block_out_channels, layer_norm_eps, ln_elementwise_affine)

self.project_to_hidden = nn.Linear(block_out_channels, hidden_size, bias=use_bias)

self.transformer_layers = nn.ModuleList(

[

BasicTransformerBlock(

dim=hidden_size,

num_attention_heads=num_attention_heads,

attention_head_dim=hidden_size // num_attention_heads,

dropout=hidden_dropout,

cross_attention_dim=hidden_size,

attention_bias=use_bias,

norm_type="ada_norm_continuous",

ada_norm_continous_conditioning_embedding_dim=hidden_size,

norm_elementwise_affine=ln_elementwise_affine,

norm_eps=layer_norm_eps,

ada_norm_bias=use_bias,

ff_inner_dim=intermediate_size,

ff_bias=use_bias,

attention_out_bias=use_bias,

)

for _ in range(num_hidden_layers)

]

)

self.project_from_hidden_norm = RMSNorm(hidden_size, layer_norm_eps, ln_elementwise_affine)

self.project_from_hidden = nn.Linear(hidden_size, block_out_channels, bias=use_bias)

self.up_block = UVitBlock(

block_out_channels,

num_res_blocks,

hidden_size,

hidden_dropout,

ln_elementwise_affine,

layer_norm_eps,

use_bias,

block_num_heads,

attention_dropout,

downsample=False,

upsample=upsample,

)

self.mlm_layer = ConvMlmLayer(

block_out_channels, in_channels, use_bias, ln_elementwise_affine, layer_norm_eps, codebook_size

)

self.gradient_checkpointing = False

def _set_gradient_checkpointing(self, module, value: bool = False) -> None:

pass

def forward(self, input_ids, encoder_hidden_states, pooled_text_emb, micro_conds, cross_attention_kwargs=None):

encoder_hidden_states = self.encoder_proj(encoder_hidden_states)

encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)

micro_cond_embeds = get_timestep_embedding(

micro_conds.flatten(), self.config.micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0

)

micro_cond_embeds = micro_cond_embeds.reshape((input_ids.shape[0], -1))

pooled_text_emb = torch.cat([pooled_text_emb, micro_cond_embeds], dim=1)

pooled_text_emb = pooled_text_emb.to(dtype=self.dtype)

pooled_text_emb = self.cond_embed(pooled_text_emb).to(encoder_hidden_states.dtype)

hidden_states = self.embed(input_ids)

hidden_states = self.down_block(

hidden_states,

pooled_text_emb=pooled_text_emb,

encoder_hidden_states=encoder_hidden_states,

cross_attention_kwargs=cross_attention_kwargs,

)

batch_size, channels, height, width = hidden_states.shape

hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)

hidden_states = self.project_to_hidden_norm(hidden_states)

hidden_states = self.project_to_hidden(hidden_states)

for layer in self.transformer_layers:

if self.training and self.gradient_checkpointing:

def layer_(*args):

return checkpoint(layer, *args)

else:

layer_ = layer

hidden_states = layer_(

hidden_states,

encoder_hidden_states=encoder_hidden_states,

cross_attention_kwargs=cross_attention_kwargs,

added_cond_kwargs={"pooled_text_emb": pooled_text_emb},

)

hidden_states = self.project_from_hidden_norm(hidden_states)

hidden_states = self.project_from_hidden(hidden_states)

hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)

hidden_states = self.up_block(

hidden_states,

pooled_text_emb=pooled_text_emb,

encoder_hidden_states=encoder_hidden_states,

cross_attention_kwargs=cross_attention_kwargs,

)

logits = self.mlm_layer(hidden_states)

return logits

@property

# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors

def attn_processors(self) -> Dict[str, AttentionProcessor]:

r"""

Returns:

`dict` of attention processors: A dictionary containing all attention processors used in the model with

indexed by its weight name.

"""

# set recursively

processors = {}

def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):

if hasattr(module, "get_processor"):

processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)

for sub_name, child in module.named_children():

fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

return processors

for name, module in self.named_children():

fn_recursive_add_processors(name, module, processors)

return processors

# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor

def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):

r"""

Sets the attention processor to use to compute attention.

Parameters:

processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):

The instantiated processor class or a dictionary of processor classes that will be set as the processor

for **all** `Attention` layers.

If `processor` is a dict, the key needs to define the path to the corresponding cross attention

processor. This is strongly recommended when setting trainable attention processors.

"""

count = len(self.attn_processors.keys())

if isinstance(processor, dict) and len(processor) != count:

raise ValueError(

f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"

f" number of attention layers: {count}. Please make sure to pass {count} processor classes."

)

def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):

if hasattr(module, "set_processor"):

if not isinstance(processor, dict):

module.set_processor(processor)

else:

module.set_processor(processor.pop(f"{name}.processor"))

for sub_name, child in module.named_children():

fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

for name, module in self.named_children():

fn_recursive_attn_processor(name, module, processor)

# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor

def set_default_attn_processor(self):

"""

Disables custom attention processors and sets the default attention implementation.

"""

if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):

processor = AttnAddedKVProcessor()

elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):

processor = AttnProcessor()

else:

raise ValueError(

f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"

)

self.set_attn_processor(processor)

class UVit2DConvEmbed(nn.Module):

def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):

super().__init__()

self.embeddings = nn.Embedding(vocab_size, in_channels)

self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)

self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)

def forward(self, input_ids):

embeddings = self.embeddings(input_ids)

embeddings = self.layer_norm(embeddings)

embeddings = embeddings.permute(0, 3, 1, 2)

embeddings = self.conv(embeddings)

return embeddings

class UVitBlock(nn.Module):

def __init__(

self,

channels,

num_res_blocks: int,

hidden_size,

hidden_dropout,

ln_elementwise_affine,

layer_norm_eps,

use_bias,

block_num_heads,

attention_dropout,

downsample: bool,

upsample: bool,

):

super().__init__()

if downsample:

self.downsample = Downsample2D(

channels,

use_conv=True,

padding=0,

name="Conv2d_0",

kernel_size=2,

norm_type="rms_norm",

eps=layer_norm_eps,

elementwise_affine=ln_elementwise_affine,

bias=use_bias,

)

else:

self.downsample = None

self.res_blocks = nn.ModuleList(

[

ConvNextBlock(

channels,

layer_norm_eps,

ln_elementwise_affine,

use_bias,

hidden_dropout,

hidden_size,

)

for i in range(num_res_blocks)

]

)

self.attention_blocks = nn.ModuleList(

[

SkipFFTransformerBlock(

channels,

block_num_heads,

channels // block_num_heads,

hidden_size,

use_bias,

attention_dropout,

channels,

attention_bias=use_bias,

attention_out_bias=use_bias,

)

for _ in range(num_res_blocks)

]

)

if upsample:

self.upsample = Upsample2D(

channels,

use_conv_transpose=True,

kernel_size=2,

padding=0,

name="conv",

norm_type="rms_norm",

eps=layer_norm_eps,

elementwise_affine=ln_elementwise_affine,

bias=use_bias,

interpolate=False,

)

else:

self.upsample = None

def forward(self, x, pooled_text_emb, encoder_hidden_states, cross_attention_kwargs):

if self.downsample is not None:

x = self.downsample(x)

for res_block, attention_block in zip(self.res_blocks, self.attention_blocks):

x = res_block(x, pooled_text_emb)

batch_size, channels, height, width = x.shape

x = x.view(batch_size, channels, height * width).permute(0, 2, 1)

x = attention_block(

x, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs

)

x = x.permute(0, 2, 1).view(batch_size, channels, height, width)

if self.upsample is not None:

x = self.upsample(x)

return x

class ConvNextBlock(nn.Module):

def __init__(

self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4

):

super().__init__()

self.depthwise = nn.Conv2d(

channels,

channels,

kernel_size=3,

padding=1,

groups=channels,

bias=use_bias,

)

self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)

self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)

self.channelwise_act = nn.GELU()

self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))

self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)

self.channelwise_dropout = nn.Dropout(hidden_dropout)

self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)

def forward(self, x, cond_embeds):

x_res = x

x = self.depthwise(x)

x = x.permute(0, 2, 3, 1)

x = self.norm(x)

x = self.channelwise_linear_1(x)

x = self.channelwise_act(x)

x = self.channelwise_norm(x)

x = self.channelwise_linear_2(x)

x = self.channelwise_dropout(x)

x = x.permute(0, 3, 1, 2)

x = x + x_res

scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)

x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]

return x

class ConvMlmLayer(nn.Module):

def __init__(

self,

block_out_channels: int,

in_channels: int,

use_bias: bool,

ln_elementwise_affine: bool,

layer_norm_eps: float,

codebook_size: int,

):

super().__init__()

self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)

self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)

self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)

def forward(self, hidden_states):

hidden_states = self.conv1(hidden_states)

hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

logits = self.conv2(hidden_states)

return logits